SoroushVahidi/scidocs huggingface dataset:
Generating regular expression signatures for network traffic classification in trusted network management
(2012)

Network traffic classification is a critical foundation for trusted network management and security systems. Matching application signatures in traffic payload is widely considered to be the most reliable classifying method. However, deriving accurate and efficient signatures for various applications is not a trivial task, for which current practice is mostly manual thus error-prone and of low efficiency. In this paper, we tackle the problem of automatic signature generation. In particular, we focus on generating regular expression signatures with a certain subset of standard syntax rules, which are of sufficient expressive power and compatible with most practical systems. We propose a novel approach that takes as input a labeled training data set and produces a set of signatures for matching the application classes presented in the data. The approach involves four procedures: pre-processing to extract application session payload, tokenization to find common substrings and incorporate position constraints, multiple sequence alignment to find common subsequences, and signature construction to transform the results into regular expressions. A real life full payload traffic trace is used to evaluate the proposed system, and signatures for a range of applications are automatically derived. The results indicate that the signatures are of high quality, and exhibit low false negatives and false positives. & 2011 Elsevier Ltd. All rights reserved.
Toward Automatic Sound Source Recognition : Identifying Musical Instruments
(1998)

average pitch average spectral centroid avg. normalized spect. cent. maximum onset slope onset duration vibrato frequency vibrato strength tremolo frequency tremolo strength slope of onset harmonic skew variance of ons. harm. skew amplitude decay odd/even harmonic ratio Features violin (bowed, muted, pizzicato) viola (bowed, muted, pizzicato) cello (bowed, muted, pizzicato) double bass (bowed, muted, pizz.) trumpet (C, Bach, C with Harmon mute) horn (normal, muted) tenor trombone (normal, muted) tuba flute piccolo oboe english horn bassoon Bb clarinet Instruments
Inversion of seismic reflection data in the acoustic approximation
(1984)

The nonlinear inverse problem for seismic reflection data is solved in the acoustic approximation. The method is based on the generalized least-squares criterion, and it can handle errors in the data set and a priori information on the model. Multiply reflected energy is naturally taken into account, as well as refracted energy or surface waves. The inverse problem can be solved using an iterative algorithm which gives, at each iteration, updated values of bulk modulus, density, and time source function. Each step of the iterative algorithm essentially consists of a forward propagation of the actual sources in the current model and a forward propagation (backward in time) of the data residuals. The correlation at each point of the space of the two fields thus obtained yields the corrections of the bulk modulus and density models. This shows, in particular, that the general solution of the inverse problem can be attained by methods strongly related to the methods of migration of unstacked data, and commercially competitive with them.
Neural Machine Translation Incorporating Named Entity
(2018)

This study proposes a new neural machine translation (NMT) model based on the encoderdecoder model that incorporates named entity (NE) tags of source-language sentences. Conventional NMT models have two problems enumerated as follows: (i) they tend to have difficulty in translating words with multiple meanings because of the high ambiguity, and (ii) these models’ ability to translate compound words seems challenging because the encoder receives a word, a part of the compound word, at each time step. To alleviate these problems, the encoder of the proposed model encodes the input word on the basis of its NE tag at each time step, which could reduce the ambiguity of the input word. Furthermore, the encoder introduces a chunk-level LSTM layer over a word-level LSTM layer and hierarchically encodes a source-language sentence to capture a compound NE as a chunk on the basis of the NE tags. We evaluate the proposed model on an English-to-Japanese translation task with the ASPEC, and English-to-Bulgarian and English-to-Romanian translation tasks with the Europarl corpus. The evaluation results show that the proposed model achieves up to 3.11 point improvement in BLEU.
Software Defect Prediction via Convolutional Neural Network
(2017)

To improve software reliability, software defect prediction is utilized to assist developers in finding potential bugs and allocating their testing efforts. Traditional defect prediction studies mainly focus on designing hand-crafted features, which are input into machine learning classifiers to identify defective code. However, these hand-crafted features often fail to capture the semantic and structural information of programs. Such information is important in modeling program functionality and can lead to more accurate defect prediction.In this paper, we propose a framework called Defect Prediction via Convolutional Neural Network (DP-CNN), which leverages deep learning for effective feature generation. Specifically, based on the programs' Abstract Syntax Trees (ASTs), we first extract token vectors, which are then encoded as numerical vectors via mapping and word embedding. We feed the numerical vectors into Convolutional Neural Network to automatically learn semantic and structural features of programs. After that, we combine the learned features with traditional hand-crafted features, for accurate software defect prediction. We evaluate our method on seven open source projects in terms of F-measure in defect prediction. The experimental results show that in average, DP-CNN improves the state-of-the-art method by 12%.
Towards continuous control of flippers for a multi-terrain robot using deep reinforcement learning
(2017)

In this paper we focus on developing a control algorithm for multi-terrain tracked robots with flippers using a reinforcement learning (RL) approach. The work is based on the deep deterministic policy gradient (DDPG) algorithm, proven to be very successful in simple simulation environments. The algorithm works in an end-to-end fashion in order to control the continuous position of the flippers. This end-to-end approach makes it easy to apply the controller to a wide array of circumstances, but the huge flexibility comes to the cost of an increased difficulty of solution. The complexity of the task is enlarged even more by the fact that real multi-terrain robots move in partially observable environments. Notwithstanding these complications, being able to smoothly control a multi-terrain robot can produce huge benefits in impaired people daily lives or in search and rescue situations. Manuscript received 22 September 2017 ar X iv :1 70 9. 08 43 0v 1 [ cs .R O ] 2 5 Se p 20 17
Computational approaches to fMRI analysis
(2017)

Analysis methods in cognitive neuroscience have not always matched the richness of fMRI data. Early methods focused on estimating neural activity within individual voxels or regions, averaged over trials or blocks and modeled separately in each participant. This approach mostly neglected the distributed nature of neural representations over voxels, the continuous dynamics of neural activity during tasks, the statistical benefits of performing joint inference over multiple participants and the value of using predictive models to constrain analysis. Several recent exploratory and theory-driven methods have begun to pursue these opportunities. These methods highlight the importance of computational techniques in fMRI analysis, especially machine learning, algorithmic optimization and parallel computing. Adoption of these techniques is enabling a new generation of experiments and analyses that could transform our understanding of some of the most complex—and distinctly human—signals in the brain: acts of cognition such as thoughts, intentions and memories.
iMAP-CampUS: Developing an Intelligent Mobile Augmented Reality Program on Campus as a Ubiquitous System
(2017)

Augmented Reality (AR) offers a combination of physical and virtual objects, drawing on the strengths of each. Therefore, it is different from virtual reality since it permits users to sight the real world enhanced with virtual objects. Thus, AR technology holds potential to support users with valuable information about the surrounding area. In this paper, we demonstrate the development of an AR app that provides the students with rich information about the buildings nearby. The application, which we name "iMAP-CampUS", is designed to assist students at Macquarie University in locating places of interest close to them by moving the camera of the device in all probable directions to overlay information of places around them. The iMAP-CampUS has been developed for both iOS and Android platforms and run on smartphones and tablets with different screen sizes.
Deep Learning Based Forecasting of Critical Infrastructure Data
(2017)

Intelligent monitoring and control of critical infrastructure such as electric power grids, public water utilities and transportation systems produces massive volumes of time series data from heterogeneous sensor networks. Time Series Forecasting (TSF) is essential for system safety and security, and also for improving the efficiency and quality of service delivery. Being highly dependent on various external factors, the observed system behavior is usually stochastic, which makes the next value prediction a tricky and challenging task that usually needs customized methods. In this paper we propose a novel deep learning based framework for time series analysis and prediction by ensembling parametric and nonparametric methods. Our approach takes advantage of extracting features at different time scales, which improves accuracy without compromising reliability in comparison with the state-of-the-art methods. Our experimental evaluation using real-world SCADA data from a municipal water management system shows that our proposed method outperforms the baseline methods evaluated here.
A fast restoration method for atmospheric turbulence degraded images using non-rigid image registration
(2013)

In this paper, a fast image restoration method is proposed to restore the true image from an atmospheric turbulence degraded video. A non-rigid image registration algorithm is employed to register all the frames of the video to a reference frame and determine the shift maps. The First Register Then Average And Subtract-variant (FRTAASv) method is applied to correct the geometric distortion of the reference frame. A performance comparison is presented between the proposed restoration method and the earlier Minimum Sum of Squared Differences (MSSD) image registration based FRTAASv method, in terms of processing time and accuracy. Simulation results show that the proposed method requires shorter processing time to achieve the same geometric accuracy.
A framework for mesencephalic dopamine systems based on predictive Hebbian learning.
(1996)

We develop a theoretical framework that shows how mesencephalic dopamine systems could distribute to their targets a signal that represents information about future expectations. In particular, we show how activity in the cerebral cortex can make predictions about future receipt of reward and how fluctuations in the activity levels of neurons in diffuse dopamine systems above and below baseline levels would represent errors in these predictions that are delivered to cortical and subcortical targets. We present a model for how such errors could be constructed in a real brain that is consistent with physiological results for a subset of dopaminergic neurons located in the ventral tegmental area and surrounding dopaminergic neurons. The theory also makes testable predictions about human choice behavior on a simple decision-making task. Furthermore, we show that, through a simple influence on synaptic plasticity, fluctuations in dopamine release can act to change the predictions in an appropriate manner.
Predictive reward signal of dopamine neurons.
(1998)

The effects of lesions, receptor blocking, electrical self-stimulation, and drugs of abuse suggest that midbrain dopamine systems are involved in processing reward information and learning approach behavior. Most dopamine neurons show phasic activations after primary liquid and food rewards and conditioned, reward-predicting visual and auditory stimuli. They show biphasic, activation-depression responses after stimuli that resemble reward-predicting stimuli or are novel or particularly salient. However, only few phasic activations follow aversive stimuli. Thus dopamine neurons label environmental stimuli with appetitive value, predict and detect rewards and signal alerting and motivating events. By failing to discriminate between different rewards, dopamine neurons appear to emit an alerting message about the surprising presence or absence of rewards. All responses to rewards and reward-predicting stimuli depend on event predictability. Dopamine neurons are activated by rewarding events that are better than predicted, remain uninfluenced by events that are as good as predicted, and are depressed by events that are worse than predicted. By signaling rewards according to a prediction error, dopamine responses have the formal characteristics of a teaching signal postulated by reinforcement learning theories. Dopamine responses transfer during learning from primary rewards to reward-predicting stimuli. This may contribute to neuronal mechanisms underlying the retrograde action of rewards, one of the main puzzles in reinforcement learning. The impulse response releases a short pulse of dopamine onto many dendrites, thus broadcasting a rather global reinforcement signal to postsynaptic neurons. This signal may improve approach behavior by providing advance reward information before the behavior occurs, and may contribute to learning by modifying synaptic transmission. The dopamine reward signal is supplemented by activity in neurons in striatum, frontal cortex, and amygdala, which process specific reward information but do not emit a global reward prediction error signal. A cooperation between the different reward signals may assure the use of specific rewards for selectively reinforcing behaviors. Among the other projection systems, noradrenaline neurons predominantly serve attentional mechanisms and nucleus basalis neurons code rewards heterogeneously. Cerebellar climbing fibers signal errors in motor performance or errors in the prediction of aversive events to cerebellar Purkinje cells. Most deficits following dopamine-depleting lesions are not easily explained by a defective reward signal but may reflect the absence of a general enabling function of tonic levels of extracellular dopamine. Thus dopamine systems may have two functions, the phasic transmission of reward information and the tonic enabling of postsynaptic neurons.
FMRI Visualization of Brain Activity during a Monetary Incentive Delay Task
(2000)

Comparative studies have implicated striatal and mesial forebrain circuitry in the generation of autonomic, endocrine, and behavioral responses for incentives. Using blood oxygen level-dependent functional magnetic resonance imaging, we sought to visualize functional activation of these regions in 12 normal volunteers as they anticipated and responded for monetary incentives. Both individual and group analyses of time-series data revealed significant activation of striatal and mesial forebrain structures (including insula, caudate, putamen, and mesial prefrontal cortex) during trials involving both monetary rewards and punishments. In addition to these areas, during trials involving punishment, group analysis revealed activation foci in the anterior cingulate and thalamus. These results corroborate comparative studies which implicate striatal and mesial forebrain circuitry in the elaboration of incentive-driven behavior. This report also introduces a new paradigm for probing the functional integrity of this circuitry in humans.
Improved Single Image Dehazing Using Dark Channel Prior and Multi-scale Retinex
(2010)

In this paper, we propose an improved image dehazing algorithm using dark channel prior and Multi-Scale Retinex. Main improvement lies in automatic and fast acquisition of transmission map of the scene. We implement the Multi-scale Retinex algorithm on the luminance component in YCbCr space, obtain a pseudo transmission map whose function is similar to the transmission map in original approach. Combining with the haze image model and the dark channel prior, we can recover a high quality haze-free image. Compared with the original method, our algorithm has two main advantages: (i) no user interaction is needed, and (ii) restoring the image much faster while maintaining comparable dehazing performance.
Formal Design and Verification of Self-Adaptive Systems with Decentralized Control
(2017)

Feedback control loops that monitor and adapt managed parts of a software system are considered crucial for realizing self-adaptation in software systems. The MAPE-K (Monitor-Analyze-Plan-Execute over a shared Knowledge) autonomic control loop is the most influential reference control model for self-adaptive systems. The design of complex distributed self-adaptive systems having decentralized adaptation control by multiple interacting MAPE components is among the major challenges. In particular, formal methods for designing and assuring the functional correctness of the decentralized adaptation logic are highly demanded.
 This article presents a framework for formal modeling and analyzing self-adaptive systems. We contribute with a formalism, called self-adaptive Abstract State Machines, that exploits the concept of multiagent Abstract State Machines to specify distributed and decentralized adaptation control in terms of MAPE-K control loops, also possible instances of MAPE patterns. We support validation and verification techniques for discovering unexpected interfering MAPE-K loops, and for assuring correctness of MAPE components interaction when performing adaptation.
Online Media Use of False News to Frame the 2016 Trump Presidential Campaign
(2017)

The 2016 U.S. presidential election campaigns witnessed an unprecedented viral false news -- a type of misinformation referred to as "factitious information blend" that is motivated to discredit political rivals. Despite the different speculations of factors that might have influenced Donald Trump's surprised victory, empirical and theoretical research on the potential impacts of false news propagated by online news media during election campaigns on influencing voters' attitudes and public opinion is seriously lacking. By drawing on the literature on framing political-effects research and by developing our computational text analytics programs, we addressed questions regarding how online news media used false news to negatively frame the Trump presidential campaign. Our text analytics results indicate that although the negative frames against Trump far outnumbered those against Hillary Clinton, weak frames of unverifiable misinformation might have failed to influence the mass audience, leaving them to the power of Trump's direct political communications via Twitter.
Seraph: an efficient, low-cost system for concurrent graph processing
(2014)

Graph processing systems have been widely used in enterprises like online social networks to process their daily jobs. With the fast growing of social applications, they have to efficiently handle massive concurrent jobs. However, due to the inherent design for single job, existing systems incur great inefficiency in memory use and fault tolerance. Motivated by this, in this paper we introduce Seraph, a graph processing system that enables efficient job-level parallelism. Seraph is designed based on a decoupled data model, which allows multiple concurrent jobs to share graph structure data in memory. Seraph adopts a copy-on-write semantic to isolate the graph mutation of concurrent jobs, and a lazy snapshot protocol to generate consistent graph snapshots for jobs submitted at different time. Moreover, Seraph adopts an incremental checkpoint/regeneration model which can tremendously reduce the overhead of checkpointing. We have implemented Seraph, and the evaluation results show that Seraph significantly outperforms popular systems (such as Giraph and Spark) in both memory usage and job completion time, when executing concurrent graph jobs.
Person Re-identification with Deep Similarity-Guided Graph Neural Network
(2018)

The person re-identification task requires to robustly estimate visual similarities between person images. However, existing person re-identification models mostly estimate the similarities of different image pairs of probe and gallery images independently while ignores the relationship information between different probe-gallery pairs. As a result, the similarity estimation of some hard samples might not be accurate. In this paper, we propose a novel deep learning framework, named Similarity-Guided Graph Neural Network (SGGNN) to overcome such limitations. Given a probe image and several gallery images, SGGNN creates a graph to represent the pairwise relationships between probegallery pairs (nodes) and utilizes such relationships to update the probegallery relation features in an end-to-end manner. Accurate similarity estimation can be achieved by using such updated probe-gallery relation features for prediction. The input features for nodes on the graph are the relation features of different probe-gallery image pairs. The probe-gallery relation feature updating is then performed by the messages passing in SGGNN, which takes other nodes’ information into account for similarity estimation. Different from conventional GNN approaches, SGGNN learns the edge weights with rich labels of gallery instance pairs directly, which provides relation fusion more precise information. The effectiveness of our proposed method is validated on three public person re-identification datasets.
Of, for, and by the people: the legal lacuna of synthetic persons
(2017)

Conferring legal personhood on purely synthetic entities is a very real legal possibility, one under consideration presently by the European Union. We show here that such legislative action would be morally unnecessary and legally troublesome. While AI legal personhood may have some emotional or economic appeal, so do many superficially desirable hazards against which the law protects us. We review the utility and history of legal fictions of personhood, discussing salient precedents where such fictions resulted in abuse or incoherence. We conclude that difficulties in holding “electronic persons” accountable when they violate the rights of others outweigh the highly precarious moral interests that AI legal personhood might protect.